{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl -LsSf https://astral.sh/uv/install.sh | sh\n",
    "!uv pip install python-a2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a terminal and run: python agent1.py\n",
    "# open another terminal and run: python agent2.py\n",
    "# open another terminal and run: python agent3.py\n",
    "\n",
    "# after that, run the following cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from python_a2a import AgentNetwork, A2AClient, AIAgentRouter\n",
    "\n",
    "# Create an agent network\n",
    "network = AgentNetwork(name=\"Math Assistant Network\")\n",
    "\n",
    "# Add agents to the network\n",
    "network.add(\"Sine\", \"http://localhost:4737\")\n",
    "network.add(\"Cosine\", \"http://localhost:4738\")\n",
    "network.add(\"Tangent\", \"http://localhost:4739\")\n",
    "\n",
    "# Create a router to intelligently direct queries to the best agent\n",
    "router = AIAgentRouter(\n",
    "    llm_client=A2AClient(\"http://localhost:5000/openai\"),  # LLM for making routing decisions\n",
    "    agent_network=network\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Route a query to the appropriate agent\n",
    "query = \"Tan of 0.78545\"\n",
    "agent_name, confidence = router.route_query(query)\n",
    "print(f\"Routing to {agent_name} with {confidence:.2f} confidence\")\n",
    "\n",
    "# Get the selected agent and ask the question\n",
    "agent = network.get_agent(agent_name)\n",
    "response = agent.ask(query)\n",
    "print(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Route a query to the appropriate agent\n",
    "query = \"Sine of 3.14159\"\n",
    "agent_name, confidence = router.route_query(query)\n",
    "print(f\"Routing to {agent_name} with {confidence:.2f} confidence\")\n",
    "\n",
    "# Get the selected agent and ask the question\n",
    "agent = network.get_agent(agent_name)\n",
    "response = agent.ask(query)\n",
    "print(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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